R Notebook

acp <- PCA(df_bio, quali.sup = sup)

Avis : Missing values are imputed by the mean of the variable: you should use the imputePCA function of the missMDA package

acp$eig

          eigenvalue percentage of variance cumulative percentage of variance
comp 1  1.197770e+01           4.436185e+01                          44.36185
comp 2  4.871503e+00           1.804260e+01                          62.40445
comp 3  3.897061e+00           1.443356e+01                          76.83801
comp 4  1.689933e+00           6.259012e+00                          83.09702
comp 5  1.174826e+00           4.351206e+00                          87.44823
comp 6  7.555345e-01           2.798276e+00                          90.24651
comp 7  6.222726e-01           2.304713e+00                          92.55122
comp 8  4.422633e-01           1.638012e+00                          94.18923
comp 9  4.024809e-01           1.490670e+00                          95.67990
comp 10 3.266903e-01           1.209964e+00                          96.88987
comp 11 2.439625e-01           9.035647e-01                          97.79343
comp 12 2.002945e-01           7.418313e-01                          98.53526
comp 13 1.510769e-01           5.595440e-01                          99.09481
comp 14 1.304115e-01           4.830057e-01                          99.57781
comp 15 3.313180e-02           1.227104e-01                          99.70052
comp 16 2.351261e-02           8.708374e-02                          99.78761
comp 17 1.789537e-02           6.627915e-02                          99.85389
comp 18 1.472727e-02           5.454546e-02                          99.90843
comp 19 9.240277e-03           3.422325e-02                          99.94265
comp 20 5.753537e-03           2.130939e-02                          99.96396
comp 21 3.934282e-03           1.457141e-02                          99.97853
comp 22 2.757545e-03           1.021313e-02                          99.98875
comp 23 1.662601e-03           6.157781e-03                          99.99491
comp 24 1.221977e-03           4.525840e-03                          99.99943
comp 25 1.491494e-04           5.524052e-04                          99.99998
comp 26 4.291986e-06           1.589625e-05                         100.00000
comp 27 9.504899e-26           3.520333e-25                         100.00000

# Créer le modèle SVM
svm_model+ <- svm(label ~ ., data = train_data, kernel = "radial")

Error: unexpected assignment in "svm_model+ <-"

print(confusion_matrix)

                       
predictions              0  1
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 [ reached getOption("max.print") -- omitted 2601 rows ]